Skill UI

A comprehensive guide for assembling a fully reproducible research package for macroeconomics papers targeting the American Economic Journal (AEJ: Macro). It ensures compliance with the AEA Data and Code Availability Policy by documenting raw data, simulation/calibration code, estimation routines, and visualization steps, guaranteeing that results can be independently verified by reviewers.

This guide outlines the comprehensive package structure required for submitting manuscripts to American Economic Review: Insights (AER: Insights). It ensures authors meet the AEA Data and Code Availability Policy, providing all necessary code, raw data access, and documentation (including a master script and exhibit-to-code map) to allow the data editor to verify the reproducibility of every result before publication. It is crucial for passing the final publication gate.

This comprehensive guide provides best practices for structuring appendices and supplementary materials for high-level academic journals. It helps authors manage the balance between main text conciseness and academic completeness by clearly defining what belongs in the core narrative (main findings) versus what should be reserved for the supplementary section (detailed proofs, extended experiments, raw data, and multimedia).

This guide details the strict data and transparency policy required by the American Political Science Review (APSR). It outlines how to build a comprehensive reproducibility package—including raw data, master scripts, and detailed documentation—to allow the journal to verify that all reported tables and figures can be regenerated before publication. It covers quantitative methods, qualitative evidence sharing, and necessary exemption procedures.

This guide provides detailed instructions on designing high-quality visual exhibits for academic literature reviews, such as the Annual Review of Sociology (ARSoc). It covers three primary exhibit types: 'Who-found-what' summary tables, conceptual framework figures, and meta-evidence plots. The focus is on synthesizing findings across multiple studies and structuring arguments, rather than presenting new primary estimates or raw data.

This guide outlines the critical rules for structuring technical research papers submitted to top-tier conferences like ASE. It emphasizes the 'decision-criticality' rule, instructing authors on how to separate core arguments (which must fit within the 10-page body) from reproduction details, raw logs, and supplementary material (the artifact). Mastering this separation is crucial for ensuring reviewers judge the paper on its merits, not its supplementary data.

This guide assists researchers in structuring and presenting academic evidence for top-tier journals like ASQ. It provides best practices for both qualitative (establishing transparent data-to-theory links, coding transparency) and quantitative (ensuring robust mechanism testing and alternative explanation checks) analyses. It ensures all theoretical claims are auditable and fully supported by the raw data.

This sophisticated tool is designed to polish scientific manuscripts for high-impact, rigorous journals. It guides users in structuring a mechanistic narrative, ensuring that every claim made in the text is precisely calibrated to the strength of the supporting experimental evidence (e.g., distinguishing correlation from necessity). It focuses on scientific storytelling, nomenclature consistency, and eliminating hype, transforming raw research data into a polished, scholarly article.

This guide outlines the standardized requirements for declaring data and code availability in major scientific publications. It details mandatory best practices, including depositing raw data (e.g., sequencing, structures) and analysis code into approved, citable public repositories (such as GEO, PDB, Mendeley Data, and Zenodo). It also provides the required standardized three-bullet statement format for ensuring reproducibility.

A comprehensive guide for creating publication-quality scientific figures, adhering to high-impact journal standards. Covers critical topics such as optimal sizing for print widths, displaying underlying raw data (dot plots, replicates), ensuring colorblind accessibility, structuring stand-alone legends, and maintaining image integrity. Essential reading for researchers preparing manuscripts.

A comprehensive guide for authors submitting to Computer Science journals, detailing how to achieve full experimental reproducibility. It systematically covers locking environments (dependencies, hardware, seeds), preventing data leakage, structuring the submission package, and creating a clear audit trail so reviewers can independently reproduce all main results from raw data.

This guide outlines the rigorous standards for creating a research replication package required by major academic journals (like The Economic Journal). It details the necessary components—including a comprehensive README, raw data, executable master code, and output logs—to ensure that all figures, tables, and results are fully reproducible by third parties. Crucial for pre-acceptance checks and maintaining academic rigor.